mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
95573eb9dd
* feat(Data): add option to predict 3 classes * feat(Evaluation): added ability to evaluate 3 class predictions * chore(Config): set sensible config for regression models * feat(Data): added option to use balanced or imbalanced three-class data * feat(Evaluate): correctly track "no_of_samples" now that we have three classes * chore(Sweep): remove probably not useful scaler values from sweep
94 lines
4.2 KiB
Python
94 lines
4.2 KiB
Python
from utils.load_data import load_data
|
|
import pandas as pd
|
|
from training.training import run_single_asset_trainig
|
|
from reporting.wandb import launch_wandb, send_report_to_wandb, register_config_with_wandb
|
|
from models.model_map import map_model_name_to_function
|
|
from feature_extractors.feature_extractor_presets import preprocess_feature_extractors_config
|
|
from config import get_default_config, validate_config, get_model_name
|
|
|
|
def setup_pipeline(project_name:str, with_wandb: bool, sweep: bool):
|
|
model_config, training_config, data_config = get_default_config()
|
|
|
|
wandb = None
|
|
if with_wandb:
|
|
wandb = launch_wandb(project_name=project_name, default_config=dict(**model_config, **training_config, **data_config), sweep=sweep)
|
|
register_config_with_wandb(wandb, model_config, training_config, data_config)
|
|
|
|
model_config = map_model_name_to_function(model_config, data_config['method'])
|
|
data_config = preprocess_feature_extractors_config(data_config)
|
|
pipeline(project_name, wandb, sweep, model_config, training_config, data_config)
|
|
|
|
|
|
|
|
def pipeline(project_name:str, wandb, sweep:bool, model_config:dict, training_config:dict, data_config:dict ):
|
|
results = pd.DataFrame()
|
|
validate_config(model_config, training_config, data_config)
|
|
|
|
for asset in data_config['all_assets']:
|
|
print('--------\nPredicting: ', asset)
|
|
all_predictions = pd.DataFrame()
|
|
|
|
# 1. Load data
|
|
data_params = data_config.copy()
|
|
data_params['target_asset'] = asset
|
|
|
|
X, y, target_returns = load_data(**data_params)
|
|
|
|
# 2. Train Level-1 models
|
|
current_result, current_predictions = run_single_asset_trainig(
|
|
ticker_to_predict = asset,
|
|
X = X,
|
|
y = y,
|
|
target_returns = target_returns,
|
|
models = model_config['level_1_models'],
|
|
method = data_config['method'],
|
|
expanding_window = training_config['expanding_window'],
|
|
sliding_window_size = training_config['sliding_window_size'],
|
|
retrain_every = training_config['retrain_every'],
|
|
scaler = training_config['scaler'],
|
|
no_of_classes = data_config['no_of_classes']
|
|
)
|
|
results = pd.concat([results, current_result], axis=1)
|
|
all_predictions = pd.concat([all_predictions, current_predictions], axis=1)
|
|
|
|
if len(model_config['level_2_models']) > 0:
|
|
# 3. Train Level-2 (Ensemble) model
|
|
|
|
ensemble_X = all_predictions
|
|
if training_config['include_original_data_in_ensemble']:
|
|
ensemble_X = pd.concat([ensemble_X, X], axis=1)
|
|
|
|
ensemble_result, ensemble_preds = run_single_asset_trainig(
|
|
ticker_to_predict = asset,
|
|
X = ensemble_X,
|
|
y = y,
|
|
target_returns = target_returns,
|
|
models = model_config['level_2_models'],
|
|
method = data_config['method'],
|
|
expanding_window = training_config['expanding_window'],
|
|
sliding_window_size = training_config['sliding_window_size'],
|
|
retrain_every = training_config['retrain_every'],
|
|
scaler = training_config['scaler'],
|
|
no_of_classes = data_config['no_of_classes']
|
|
)
|
|
|
|
results = pd.concat([results, ensemble_result], axis=1)
|
|
all_predictions = pd.concat([all_predictions, ensemble_preds], axis=1)
|
|
|
|
# 4. Save & report results
|
|
send_report_to_wandb(results, wandb, project_name, get_model_name(model_config))
|
|
results.to_csv('results.csv')
|
|
|
|
level1_columns = results[[column for column in results.columns if 'Ensemble' not in column]]
|
|
ensemble_columns = results[[column for column in results.columns if 'Ensemble' in column]]
|
|
|
|
print("Mean no of samples: ", results.loc['no_of_samples'].mean())
|
|
print("Mean Sharpe ratio for Level-1 models: ", round(level1_columns.loc['sharpe'].mean(), 3))
|
|
print("Mean Sharpe ratio for Level-2 (Ensemble) models: ", round(ensemble_columns.loc['sharpe'].mean(), 3))
|
|
|
|
if sweep:
|
|
if wandb.run is not None:
|
|
wandb.finish()
|
|
|
|
if __name__ == '__main__':
|
|
setup_pipeline(project_name='price-prediction', with_wandb = False, sweep = False) |